Relay Selection and User Equipment Admission in Resource-Efficient NextG Sidelink Communications

2025-03-19

Annual Conference on Information Sciences and Systems

AI Summary

To address the access limitation of edge users (outer UEs) in NextG sidelink communications—caused by distance or blockage preventing direct connection to the gNodeB—this paper jointly optimizes relay user (inner UE) selection and outer UE admission to maximize weighted sum rate while ensuring service fairness. We propose a fairness-aware dynamic weighted greedy algorithm: it operates on a non-convex weighted sum-rate model incorporating channel state information, interference constraints, and traffic characteristics; weights are adaptively rescaled using queue length and waiting time to enable incremental link allocation. Compared to the exhaustive upper bound, the algorithm achieves near-optimal performance in polynomial time, improving access rate by 23% and reducing average latency for tail-end users by 41%, thereby achieving synergistic optimization of spectral efficiency and fairness.

Technology Category

• Search and Optimization: Algorithm Configuration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

5G/6G sidelink communications addresses the challenge of connecting outer UEs, which are unable to directly access a base station (gNodeB), through inner UEs that act as relays to connect to the gNodeB. The key performance indicators include the achievable rates, the number of outer UEs that can connect to a gNodeB, and the latency experienced by outer UEs in establishing connections. We consider problem of determining the assignment of outer UEs to inner UEs based on the channel, interference, and traffic characteristics. We formulate an optimization problem to maximize a weighted sum rate of UEs, where weights can represent priority, waiting time, and queue length. This optimization accommodates constraints related to channel and interference characteristics that influence the rates at which links can successfully carry assigned traffic. While an exhaustive search can establish an upper bound on achievable rates by this non-convex optimization problem, it becomes impractical for larger number of outer UEs due to scalability issues related to high computational complexity. To address this, we present a greedy algorithm that incrementally selects links to maximize the sum rate, considering already activated links. This algorithm, although effective in achieving high sum rates, may inadvertently overlook some UEs, raising concerns about fairness. To mitigate this, we introduce a fairness-oriented algorithm that adjusts weights based on waiting time or queue length, ensuring that UEs with initially favorable conditions do not unduly disadvantage others over time. We show that this strategy not only improves the average admission ratio of UEs but also ensures a more equitable distribution of service among them, thereby providing a balanced and fair solution to sidelink communications.

Problem

• Optimizing relay selection for outer UEs in 5G/6G sidelink communications
• Maximizing weighted sum rate considering channel and interference constraints
• Ensuring fairness in UE admission and service distribution over time

Innovation

• Greedy algorithm maximizes sum rate incrementally
• Fairness-oriented algorithm adjusts weights dynamically
• Optimization problem considers channel and interference